Systems and methods for time use optimization

ABSTRACT

Systems and methods for time use optimization are provided. One embodiment of a method includes determining time of use pricing data associated with purchase of energy from an energy provider, partitioning a predetermined amount of time into a plurality of segments, where the plurality of segments corresponds with the higher cost tier and the lower cost tier, and creating an energy set point schedule for setting a set point of a controllable device, where the energy set point schedule sets the set point of the controllable device to a predetermined value for each of the plurality of segments. Some embodiments include determining energy utilized by the controllable device during at least a portion of the energy set point schedule and iteratively altering the energy set point schedule, based on a comparison of the energy utilized and a current status of the energy set point schedule.

CROSS REFERENCE

This application is a continuation of U.S. patent Ser. No. 15/819,295filed Nov. 21, 2017, which claims the benefit of U.S. Provisional Ser.No. 62/427,554, filed Nov. 29, 2016, all of which are incorporatedherein by reference in its entirety.

TECHNICAL FIELD

Embodiments described herein generally relate to systems and methods fortime use optimization and, more specifically, to systems and methods foroptimizing use of a water heater or other controllable device.

BACKGROUND

Household devices, commercial devices, and other controllable devicesmay store energy, such as thermal energy, electrical energy, potentialenergy, etc. As an example, water heaters and heating ventilation, airconditioning (HVAC) systems may store thermal energy. Batteries andcapacitors may store electrical energy. Additionally, many energycompanies now offer price structures such that incentivize an energyuser to consume energy at predetermined periods during a day. As such,those devices that operate consistently throughout the day may notoperate as financially efficiently as possible.

As an example, a water heater typically stores energy in the form of hotwater for usage at a later time. Because water heaters are often wellinsulated, it often does not matter when the water is heated, so long asthere is sufficient hot water when the user needs to use the water. As aresult, oftentimes the water in a water heater could be heated at anytime of day.

SUMMARY

Systems and methods for time use optimization are provided. Oneembodiment of a method includes determining time of use pricing dataassociated with purchase of energy from an energy provider, partitioninga predetermined amount of time into a plurality of segments, where theplurality of segments corresponds with the higher cost tier and thelower cost tier, and creating an energy set point schedule for setting aset point of a controllable device, where the energy set point schedulesets the set point of the controllable device to a predetermined valuefor each of the plurality of segments. Some embodiments includedetermining energy utilized by the controllable device during at least aportion of the energy set point schedule and iteratively altering theenergy set point schedule, based on a comparison of the actual energyusage and a current status of the energy set point schedule.

Embodiments of a system include a controllable device that includes anenergy storage component for storing energy and an energy distributioncomponent for distributing the energy to an environment, where theenergy is purchased from an energy provider. The system may also includea computing device that includes a processor and a memory component. Thememory component may store logic that, when executed by the processor,causes the system to receive time of use pricing data associated withpurchase of the energy from the energy provider, where the time of usepricing data includes a higher cost tier associated with a higher costfor the energy during a first predetermined time period and a lower costtier associated with a lower cost for the energy during a secondpredetermined time period. The logic may further cause the system todetermine a desired energy output for the controllable device and createan energy set point schedule that controls a setting of the controllabledevice, such that the energy set point schedule causes the computingdevice to adjust the setting of the controllable device at predeterminedsegments to maintain a desired energy set point for the controllabledevice, while factoring the time of use pricing data. The logic mayfurther cause the system to collect data regarding an actual demand forthe energy, where the data regarding the actual demand includes anamount of energy actually used, a time of actual use, and a cost of theenergy actually used, based on the time of use pricing data anditeratively alter the energy set point schedule based on the dataregarding the actual demand for the energy.

Embodiments of a non-transitory computer-readable medium are alsoprovided. Some embodiments include logic that, when executed by acomputing device causes the computing device to determine time of usepricing data associated with purchase of energy from an energy provider,where the time of use pricing data includes a higher cost tierassociated with a higher cost for the energy during a firstpredetermined time period and a lower cost tier associated with a lowercost for the energy during a second predetermined time period. The logicmay further cause the computing device to partition a predeterminedamount of time into a plurality of segments, where the plurality ofsegments corresponds with the higher cost tier and the lower cost tierand create an energy set point schedule for setting a set point of acontrollable device, where the energy set point schedule sets the setpoint of the controllable device to a predetermined value for each ofthe plurality of segments. In some embodiments, the logic causes thecomputing device to, after conclusion of the first predetermined timeperiod, compute energy utilized by the controllable device during asegment associated with the higher cost tier and, in response to adetermination that energy used during the first predetermined timeperiod is greater than about zero, adjust an energy set point of asegment that precedes the higher cost tier to a higher value.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments set forth in the drawings are illustrative and exemplaryin nature and not intended to limit the disclosure. The followingdetailed description of the illustrative embodiments can be understoodwhen read in conjunction with the following drawings, where likestructure is indicated with like reference numerals and in which:

FIG. 1 depicts a computing environment for time of use optimization,according to embodiments described herein;

FIG. 2 depicts a time of use schedule that may be implemented by autility, according to embodiments described herein;

FIG. 3 depicts a user heating schedule, according to embodimentsdescribed herein;

FIG. 4 depicts a flowchart for time of use optimization, according toembodiments described herein;

FIG. 5 depicts another flowchart for time of use optimization, accordingto embodiments described herein; and

FIG. 6 depicts a remote computing device for time use optimization,according to embodiments described herein.

DETAILED DESCRIPTION

Embodiments disclosed herein include systems and methods for time of useoptimization. As an example, water heaters typically include a largetank of water, a heater element, and a control system for regulating thetemperature of water in the tank. Some water heaters use electricresistance elements and some also use a heat pump to heat the water. Thetemperature in the tank decreases in response to two stimuli: conductivelosses between the hot water and the outside air and the mixing ofincoming cooler water after removal of hot water by the user. Thecontrol system attempts to keep the temperature in the tank close to atarget (or set point) temperature. Embodiments of other controllabledevices are also described herein.

A time of use (TOU) electricity rate is a pricing structure forelectricity that changes based on the time of day and day of the week.Typically, the price of energy ($/kilowatt-hour) would be less expensiveat night and more expensive during the work-day.

The typical user keeps a fairly predictable schedule, and thispredictability can be leveraged to reduce the cost of heating water withtime of use rates. Embodiments described herein include an iterativelearning algorithm for adjusting the water temperature set point. Thelearning algorithm may be configured to learn a 7-day set point schedule(or other schedule) to minimize the cost of heating water.

Accordingly, some embodiments may be configured to propose at least onegoal and/or objective of the water heater performance (e.g. keeptemperature above 100 degrees Fahrenheit and minimize cost).Additionally, embodiments may be configured to partition the day intosegments and group segments (periods) by the utility cost. At the startof a high-price period, the set point may be set to a lower thresholdtemperature. At the end of the high-price period, the electrical energyused during the period may be computed. If energy used is greater 0, theset point temperature of the segment preceding the high-priced periodmay be adjusted to a higher value. If energy used equals 0, the segmentpreceding the high-priced period may be adjusted to a lower temperature.In some embodiments, this may be iteratively adjusted until cost andcomfort are optimized. The systems and methods for time use optimizationincorporating the same will be described in more detail, below.

Additionally, other embodiments of controllable devices may utilizesimilar processes to utilize time of use incentives advantageously. Asan example, an electric battery may be configured to pull electricalenergy from a utility, based on a balance of cost and anticipateddemand. Determinations may also be made regarding wither the capacity ofthe controllable device is sufficient to fully optimize the time of useprice structure and energy demand. If not, embodiments may provide arecommendations for upgrade.

Referring now to the drawings, FIG. 1 depicts a computing environmentfor time of use optimization, according to embodiments described herein.As illustrated, a network 100 may facilitate communication between ofenergy from a utility 106 and/or data among a controllable device 102, aremote computing device 104, and a utility 106. As such, the network 100may include an electricity network to provide electrical power to homesand businesses, a gas line network to provide natural gas to homes andbusinesses, a wide area network, such as the internet, a local network,etc. Additionally, the controllable device 102 is depicted as a tankwater heater, but may include any device that is subject to time of usescheduling and is controllable according to a predetermined schedule,such as a battery, a home ventilation air condition (HVAC) system,and/or other device.

As illustrated, the controllable device 102 may include a tank 108 (orcell, depending on the embodiment), a local computing device 110, and aheating element 112. The tank 108 may be configured for receiving waterfor heating. The heating element 112 may be configured for electricaland/or gas heating, depending on the embodiment, and may include athermometer and/or thermostat to measure and regulate the temperature ofthe water. The local computing device 110 may be configured to receiveand/or determine a schedule for time of use, user schedule and/orheating, and communicate with the thermostat to regulate thetemperature, as described herein.

The remote computing device 104 may be coupled to the utility 106 and/orthe controllable device 102 and may include a memory component 140 thatstores time of use logic 144 a and schedule logic 144 b. The time of uselogic 144 a may be configured to cause the remote computing device 104to receive and/or determine a time of use schedule that is implementedby the utility 106. The time of use logic 144 a may additionally causethe remote computing device 104 to determine a user schedule associatedwith the controllable device 102. The schedule logic 144 b may cause theremote computing device 104 to determine a schedule for activating theheating element 112 to maximize efficiency and reduce cost, while stillmaintaining a user-defined level of service by the controllable device102.

It should be understood that while the remote computing device 104, thetime of use logic 144 a, and the schedule logic 144 b are depicted asbeing remote from the controllable device 102, this is merely anexample. Some embodiments are configured such that at least a portion ofthis functionality is provided by the local computing device 110.

FIG. 2 depicts a time of use schedule that may be implemented by autility 106, according to embodiments described herein. As illustrated,a time of use schedule may be communicated to the remote computingdevice 104 by the utility 106.

FIG. 3 depicts a user heating schedule, according to embodimentsdescribed herein. Depending on the particular embodiment, the remotecomputing device 104 and/or the local computing device 110 may monitorusage of the controllable device 102 to determine the schedule in FIG.3. In some embodiments, the remote computing device 104 may provide auser interface for the user to specifically input a desired scheduleand/or edit an existing schedule. Regardless, based on the informationin the user heating schedule and the time of use schedule of FIG. 2, acalculation may be performed to determine when the heating element 112should be active to heat the water, such that the controllable device102 can provide water at a predetermined temperature at the determineduser heating schedule. As such, some embodiments of the remote computingdevice 104 may provide the user with a user interface to specify adesired temperature during times of usage. Depending on the embodiment,this option may be provided on the controllable device 102. The remotecomputing device 104 may also monitor the water temperature to determinea rate of temperature drop in the controllable device 102.

As an example, if the user specifies that the desired water temperaturefrom the controllable device 102 should be 100 degrees Fahrenheit, theremote computing device 104 may determine how long the heating element112 should heat the water to achieve the set point temperature duringthe times of high usage. Additionally, the remote computing device 104may also consider the time of usage schedule to select the most costeffective heating schedule to achieve both the set point and the costefficiency.

FIG. 4 depicts a flowchart for time of use optimization, according toembodiments described herein. As illustrated in block 450, a time of useschedule may be determined. In block 452, a user heating schedule may bedetermined. In block 454, a cooling rate of the water may be determined.In block 456, the time of use schedule, the user heating schedule, andthe cooling rate may be utilized to determine a heating schedule thatmeets a predetermined set point, as well as meets the desired costefficiency. In block 458, the heating schedule may be implemented. Inblock 460, the current cost may be determined, as well as whetheranother heating schedule can achieve the same level of service, with alower cost. In block 462, in response to determining that the otherheating schedule is more efficient, the other heating schedule may beimplemented.

FIG. 5 depicts another flowchart for time of use optimization, accordingto embodiments described herein. As illustrated in block 550, goals forthe system may be determined. Depending on the embodiment, the goal isutilized to determine a characteristic of the energy set point schedule.In block 552, a day may be partitioned into segments and the segmentsmay be grouped by energy cost tier. In block 554, at the beginning of ahigher cost tier, the energy set point may be set to a firstpredetermined value. In block 556, at the end of the higher cost tier,energy utilized during the period may be computed. As will beunderstood, reference to energy being utilized may include externalenergy added to the system, energy removed from the system from basiclosses, energy removed from the system by usage, e.g. people using hotwater, a difference in energy from the desired setting and the currentsetting at the end of the time period. In a water heater example, thiscould be the difference in temperature from the set point and thecurrent temperature. This may represent excess or deficient systemenergy.

In block 558, if the energy used during the first predetermined timeperiod is greater than about 0, the energy set point of the segmentpreceding the higher cost tier may be adjusted to a higher value.Additionally, in response to a determination that energy used during thefirst predetermined time period is greater than about zero, the energyset point schedule may be altered at the first predetermined time periodto a lower value. In response to a determination that energy used duringthe first predetermined time period is less than about zero, the energyset point of a segment that precedes the higher cost tier may beadjusted to a lower value.

Depending on the particular embodiment, the energy set point schedulemay be altered based on the following: T_(i)(k+1)=T_(i)(k)−ργE_(i)(k);T_(i−1)(k+1)=T_(i−1)(k)+εγE_(i)(k) when P_(i−1)<P_(i);T_(i)(k+1)=T_(i)(k); T_(i−1)(k+1)=T_(i−1)(k) when P_(i−1)>=P_(i); wherek is a current iteration, T_(i) is a scheduled state variable at a timesegment i, E_(i) is a change in energy over the time segment i, ρ>0 andε>0 are learning constants, and γ is a conversion from energy to statevariable. Other formulations for determining the energy set pointschedule may also be utilized.

FIG. 6 depicts a remote computing device 104 for time use optimization,according to embodiments described herein. As illustrated, the remotecomputing device 104 includes a processor 630, input/output hardware632, the network interface hardware 634, a data storage component 636(which stores schedule data 638 a, user data 638 b, and/or other data),and the memory component 140. The memory component 140 may be configuredas volatile and/or nonvolatile memory and as such, may include randomaccess memory (including SRAM, DRAM, and/or other types of RAM), flashmemory, secure digital (SD) memory, registers, compact discs (CD),digital versatile discs (DVD), and/or other types of non-transitorycomputer-readable mediums. Depending on the particular embodiment, thesenon-transitory computer-readable mediums may reside within the remotecomputing device 104 and/or external to the remote computing device 104.

The memory component 140 may store operating logic 642, the time of uselogic 144 a, and the schedule logic 144 b. The time of use logic 144 aand the schedule logic 144 b may each include a plurality of differentpieces of logic, each of which may be embodied as a computer program,firmware, and/or hardware, as an example. A local interface 646 is alsoincluded in FIG. 6 and may be implemented as a bus or othercommunication interface to facilitate communication among the componentsof the remote computing device 104.

The processor 630 may include any processing component operable toreceive and execute instructions (such as from a data storage component636 and/or the memory component 140). The input/output hardware 632 mayinclude and/or be configured to interface with microphones, speakers, adisplay, and/or other hardware.

The network interface hardware 634 may include and/or be configured forcommunicating with any wired or wireless networking hardware, includingan antenna, a modem, LAN port, wireless fidelity (Wi-Fi) card, WiMaxcard, ZigBee card, Bluetooth chip, USB card, mobile communicationshardware, and/or other hardware for communicating with other networksand/or devices. From this connection, communication may be facilitatedbetween the remote computing device 104 and other computing devices,such as the controllable device 102.

The operating logic 642 may include an operating system and/or othersoftware for managing components of the remote computing device 104. Asalso discussed above, time of use logic 144 a and the schedule logic 144b may reside in the memory component 140 and may be configured toperform the functionality, as described herein.

It should be understood that while the components in FIG. 6 areillustrated as residing within the remote computing device 104, this ismerely an example. In some embodiments, one or more of the componentsmay reside external to the remote computing device 104. It should alsobe understood that, while the remote computing device 104 is illustratedas a single device, this is also merely an example. In some embodiments,the time of use logic 144 a and the schedule logic 144 b may reside ondifferent computing devices. As an example, one or more of thefunctionalities and/or components described herein may be provided by aremote computing device 104 and/or local computing device 110, which maybe coupled to the remote computing device 104 via the network 100.

Additionally, while the remote computing device 104 is illustrated withthe time of use logic 144 a and the schedule logic 144 b as separatelogical components, this is also an example. In some embodiments, asingle piece of logic (and/or or several linked modules) may cause theremote computing device 104 to provide the described functionality.

As illustrated above, various embodiments time of use optimization aredisclosed. These embodiments may cause the user to experience consistentperformance of a controllable device at a lower cost, based on time ofuse scheduling.

While particular embodiments and aspects of the present disclosure havebeen illustrated and described herein, various other changes andmodifications can be made without departing from the spirit and scope ofthe disclosure. Moreover, although various aspects have been describedherein, such aspects need not be utilized in combination. Accordingly,it is therefore intended that the appended claims cover all such changesand modifications that are within the scope of the embodiments shown anddescribed herein.

It should now be understood that embodiments disclosed herein includessystems, methods, and non-transitory computer-readable mediums for timeof use optimization. It should also be understood that these embodimentsare merely exemplary and are not intended to limit the scope of thisdisclosure.

What is claimed is:
 1. A method for time of use optimization comprising:determining time of use pricing data associated with purchase of energyfrom an energy provider, wherein the time of use pricing data includes ahigher cost tier associated with a higher cost for the energy during afirst time period and a lower cost tier associated with a lower cost forthe energy during a second time period; partitioning a predeterminedamount of time into a first plurality of segments and a second pluralityof segments, wherein at least one of the first plurality of segmentscorresponds with the higher cost tier and at least one of the secondplurality of segments corresponds with the lower cost tier; creating anenergy set point schedule for setting a set point of a controllabledevice, wherein the energy set point schedule sets the set point of thecontrollable device to a predetermined value for each of the firstplurality of segments and the second plurality of segments, whilemaintaining operation and control of the controllable device through allof the energy set point schedule; determining whether energy purchasedby the controllable device during the higher cost tier is greater thanabout zero; and in response to determining that the energy purchased bythe controllable device during the higher cost tier is greater thanabout zero, iteratively altering an energy set point temperature of asegment that precedes the higher cost tier to a higher value.
 2. Themethod of claim 1, wherein the controllable device includes at least oneof the following: a water heater, a home ventilation and air condition(HVAC) system, or a battery.
 3. The method of claim 1, wherein theenergy set point schedule is altered based on the following:T _(i)(k+1)=T _(i)(k)−ργE _(i)(k); T _(i−1)(k+1)=T _(i−1)(k)+εγE _(i)(k)when P _(i−1) <P _(i)T _(i)(k+1)=T _(i)(k); T _(i−1)(k+1)=T _(i−1)(k) when P _(i−1) >=P _(i)wherein k is a current iteration, T_(i) is a scheduled state variable ata time segment i, E_(i) is a change in energy over a time segment i, ρ>0and ε>0 are learning constants, P_(i) is the price of energy at timesegment i, and γ is a conversion from energy to state variable.
 4. Themethod of claim 1, further comprising altering the energy set pointschedule to maintain a desired energy output while minimizing costincurred for the energy.
 5. The method of claim 1, wherein the energyset point schedule includes segments for each day of a week.
 6. Themethod of claim 1, further comprising receiving a goal for the energyset point schedule, wherein the goal is utilized to determine acharacteristic of the energy set point schedule.
 7. A system for time ofuse optimization comprising: a controllable device that includes anenergy storage component for storing energy and an energy distributioncomponent for distributing the energy to an environment, wherein theenergy is purchased from an energy provider; and a computing device thatincludes a processor and a memory component, wherein the memorycomponent stores logic that, when executed by the processor, causes thesystem to perform at least the following: receive time of use pricingdata associated with purchase of the energy from the energy provider,wherein the time of use pricing data includes a higher cost tierassociated with a higher cost for the energy during a first time periodand a lower cost tier associated with a lower cost for the energy duringa second time period; determine a desired energy output for thecontrollable device; create an energy set point schedule that controls aset point setting of the controllable device, such that the energy setpoint schedule causes the computing device to adjust the set pointsetting of the controllable device at predetermined segments to maintaina desired energy set point for the controllable device, whilemaintaining operation and control of the controllable device through allof the energy set point schedule, and while factoring the time of usepricing data; collect data regarding an actual demand for the energy,wherein the data regarding the actual demand includes an amount ofenergy actually used, a time of actual use, and a cost of the energyactually purchased, based on the time of use pricing data; and inresponse to a determination that energy purchased during the first timeperiod is greater than about zero, iteratively alter a temperature ofthe energy set point schedule based on the data regarding the actualdemand for the energy.
 8. The system of claim 7, wherein thecontrollable device includes at least one of the following: a waterheater, a home ventilation and air condition (HVAC) system, or abattery.
 9. The system of claim 7, wherein the energy set point scheduleis altered based on the following:T _(i)(k+1)=T _(i)(k)−ρεE _(i)(k); T _(i−1)(k+1)=T _(i−1)(k)+εγE _(i)(k)when P _(i−1) <P _(i)T _(i)(k+1)=T _(i)(k); T _(i−1)(k+1)=T _(i−1)(k) when P _(i−1) >=P _(i)wherein k is a current iteration, T_(i) is a scheduled state variable ata time segment i, E_(i) is a change in energy over a time segment i, ρ>0and ε>0 are learning constants, P_(i) is the price of energy at timesegment i, and γ is a conversion from energy to state variable.
 10. Thesystem of claim 7, wherein the logic further causes the system to alterthe energy set point schedule to maintain the desired energy outputwhile minimizing cost incurred for the energy.
 11. The system of claim7, wherein in response to a determination that energy purchased duringthe first time period is greater than about zero, the logic causes thesystem to alter the energy set point schedule at the first time periodto a lower value.
 12. The system of claim 7, wherein in response to adetermination that energy purchased during the first time period isgreater than about zero, the logic causes the system to adjust an energyset point of a segment that precedes the higher cost tier to a highervalue.
 13. The system of claim 7, wherein in response to a determinationthat energy purchased during the first time period is less than aboutzero, adjust an energy set point of a segment that precedes the highercost tier to a lower value.
 14. A non-transitory computer-readablemedium that stores logic that, when executed by a computing devicecauses the computing device to perform at least the following: determinetime of use pricing data associated with purchase of energy from anenergy provider, wherein the time of use pricing data includes a highercost tier associated with a higher cost for the energy during a firsttime period and a lower cost tier associated with a lower cost for theenergy during a second time period; partition a predetermined amount oftime into a plurality of segments, wherein the plurality of segmentscorresponds with the higher cost tier and the lower cost tier; create anenergy set point schedule for setting a set point of a controllabledevice, wherein the energy set point schedule sets the set point of thecontrollable device to a value for each of the plurality of segments,while maintaining operation and control of the controllable devicethrough all of the energy set point schedule; compute energy purchasedby the controllable device during a segment associated with the highercost tier; and in response to a determination that energy purchasedduring the first time period is greater than about zero, adjust anenergy set point of a segment that precedes the higher cost tier to ahigher value.
 15. The non-transitory computer-readable medium of claim14, wherein the controllable device includes at least one of thefollowing: a water heater, a home ventilation and air condition (HVAC)system, or a battery.
 16. The non-transitory computer-readable medium ofclaim 14, wherein the energy set point schedule is created based on thefollowing:T _(i)(k+1)=T _(i)(k)−ργE _(i)(k); T _(i−1)(k+1)=T _(i−1)(k)+εγE _(i)(k)when P _(i−1) <P _(i)T _(i)(k+1)=T _(i)(k); T _(i−1)(k+1)=T_(i−1)(k) when P _(i−1) >=P _(i)wherein k is a current iteration, T_(i) is a scheduled state variable ata time segment i, E_(i) is a change in energy over a time segment i, ρ>0and ε>0 are learning constants, P_(i) is the price of energy at timesegment i, and γ is a conversion from energy to state variable.
 17. Thenon-transitory computer-readable medium of claim 14, wherein the logiccauses the computing device to alter the energy set point schedule tomaintain a desired energy output while minimizing cost incurred for theenergy.
 18. The non-transitory computer-readable medium of claim 14,wherein the energy set point schedule includes segments for each day ofa week.
 19. The non-transitory computer-readable medium of claim 14,wherein the logic further causes the computing device to group theplurality of segments according to the higher cost tier and the lowercost tier.
 20. The non-transitory computer-readable medium of claim 14,wherein the energy set point schedule is iteratively adjusted, based onan actual energy usage.